Lessons Learned from the Development and Implementation of AI Forecast Guidance at the U.S. National Weather Service’s Storm Prediction Center

Artificial intelligence (AI) and machine learning (ML) tools are rapidly growing in capability and application across the weather enterprise. Fully AI-based numerical weather prediction (NWP) emulators are beginning to outperform traditional NWP, and many weather agencies have started to adopt ML-derived guidance products into the forecast process. For example, the United States National Weather Service’s Storm Prediction Center (SPC) has implemented a number of ML models to aid in the prediction and detection of tornadoes, severe wind, hail, and wildfires. However, the development of these AI/ML products and their subsequent transition into SPC operations revealed several challenges which potentially slowed their overall adoption into the forecasters’ workflow. This presentation will discuss several factors that impacted the adoption of AI/ML into forecast operations and highlight some best practices used by SPC to help streamline the research-to-operations transition. Case studies of AI/ML projects that were successfully transitioned into SPC operations will help illustrate the application of these best practices and showcase some of the common pitfalls faced by AI/ML development for operational applications.